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Fishery Bulletin 95 ( 1 ), 1997 
adjust the results in order to maintain that symme- 
try. However, if model constraints or other features 
of the model or data force the response surface to 
have an underlying (but unknown) skewed distribu- 
tion, then the outlier selection process outlined here 
might falsely identify some data points as outliers. 
Conversely, the least trimmed squares (LTS) solu- 
tions make no assumptions about the shape of the 
response surface. Therefore, we expect that the LTS 
method could be robust to those situations where the 
distribution is skewed. Nevertheless, with judicious 
application, robust regression is expected to be a 
useful tool for evaluating and selecting data appro- 
priate for tuning stock assessment models. 
Acknowledgments 
We are grateful to two anonymous reviewers for their 
critical review of this manuscript. Support for this 
study was provided through the Cooperative Unit for 
Fisheries Education and Research (CUFER) by Na- 
tional Oceanic and Atmospheric Administration Co- 
operative Agreement NA90-RAH-0075. 
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